Transmembrane protein alignment and fold recognition based on predicted topology.
Transmembrane protein alignment and fold recognition based on predicted topology.
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基于预测拓扑结构的跨膜蛋白比对和折叠识别。
DOI：
10.1371/journal.pone.0069744
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Xu D
中科院分区：
文献类型：
--
作者：
Wang H;He Z;Zhang C;Zhang L;Xu D
尽管跨膜蛋白（TMP）在各种生物过程和药物开发中非常重要，但TMP结构的一般预测仍然远不能令人满意。由于TMP具有与可溶性蛋白质显著不同的物理化学性质，因此目前用于可溶性蛋白质的蛋白质结构预测工具可能不适用于TMP。随着TMP实验结构的不断增加，基于模板的方法有可能成为广泛适用于TMP结构预测。然而，目前TMP的折叠识别方法并不像可溶性蛋白那样发达。我们开发了一种新的TMP折叠识别方法，TMFR，识别TMP折叠的基础上，序列到结构成对比对。该方法利用基于拓扑结构的特征与序列概况和溶剂可及性一起进行比对。它还结合了一个缺口罚分，这取决于预测的拓扑结构片段。考虑到α-螺旋跨膜蛋白（αTMP）和β-链跨膜蛋白（βTMP）之间的差异，在非冗余训练数据集中分别使用58个α TMP和17个β TMP训练这两种蛋白类别的评分函数参数。我们将我们的方法与HHalign进行了比较，HHalign是一种领先的比对工具，使用包括72个α TMP和30个β TMP的非冗余测试数据集。我们的方法在α TMP和β TMP中的准确率分别比HHalign高10%和9%。TMFR生成的原始分数与目标和模板之间的结构相似性呈负相关，这表明其用于折叠识别的有效性。结果表明TMFR提供了一种有效的TMP特异性折叠识别和比对方法。
Although Transmembrane Proteins (TMPs) are highly important in various biological processes and pharmaceutical developments, general prediction of TMP structures is still far from satisfactory. Because TMPs have significantly different physicochemical properties from soluble proteins, current protein structure prediction tools for soluble proteins may not work well for TMPs. With the increasing number of experimental TMP structures available, template-based methods have the potential to become broadly applicable for TMP structure prediction. However, the current fold recognition methods for TMPs are not as well developed as they are for soluble proteins. We developed a novel TMP Fold Recognition method, TMFR, to recognize TMP folds based on sequence-to-structure pairwise alignment. The method utilizes topology-based features in alignment together with sequence profile and solvent accessibility. It also incorporates a gap penalty that depends on predicted topology structure segments. Given the difference between α-helical transmembrane protein (αTMP) and β-strands transmembrane protein (βTMP), parameters of scoring functions are trained respectively for these two protein categories using 58 αTMPs and 17 βTMPs in a non-redundant training dataset. We compared our method with HHalign, a leading alignment tool using a non-redundant testing dataset including 72 αTMPs and 30 βTMPs. Our method achieved 10% and 9% better accuracies than HHalign in αTMPs and βTMPs, respectively. The raw score generated by TMFR is negatively correlated with the structure similarity between the target and the template, which indicates its effectiveness for fold recognition. The result demonstrates TMFR provides an effective TMP-specific fold recognition and alignment method.
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